Gut Microbiome and Gastrointestinal Diseases
Bibliographic record
Abstract
연세대학교 원주캠퍼스 보건과학대학 임상병리학과The gut microbiome has been studied extensively over the past decade with most scientific reports focused on the adverse role of the gut microbiome on gastrointestinal diseases.For example, the altered gut microbiome exacerbates the development of immune system-mediated damage in many diseases.The most studied pathologies include irritable bowel syndrome, inflammatory bowel diseases, and colitis-associated cancer.On the other hand, intestinal microflora is also beneficial and contributes to the intestinal physiology by the synthesis of vitamins, production of short chain fatty acids and bile acid metabolism, thereby maintaining gut homeostasis.Therefore, the balance between commensal and pathogenic bacteria populations influences mainly the maintenance of intestinal health.Changes in the intestinal microflora have been suspected to be the underlying causes of multiple diseases.Despite the immense amount of published data, the optimal gut microbiome composition is still controversial.This review briefly outlines the connection between the gut microbiome and critical gastrointestinal diseases focusing on three prominent intestinal disorders: irritable bowel syndrome, inflammatory bowel diseases, and colitis-associated cancer disorders.Finally, intervention strategies using natural products for the alleviation of these diseases and the maintenance of a health gut microbiome are suggested.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".